Ensembles can have a major impact on model compatibility, this has been empirically reported in other works such as [1]}, and this simple analysis provides an explanation for their effectiveness. We empirically showcase the effect ensembles have on several compatibility metrics on CelebA [2]} and CIFAR-10 [3]}, where we train several independent ResNet networks [4]} (ResNet-18 for Cifar-10 and Resnet-34 for CelebA) on the same training dataset using SGD (effectively taking independent samples from the SGD learning kernel). We used a simple ensemble technique where the average output of \(n_e\)  independent networks constitute our ensemble model. We show how total compatibility and positive compatibility are effectively managed via ensembling in Table REF .
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